The collection of information relating to downhole conditions, commonly referred to as “logging,” can be performed by several methods including “logging while drilling” (“LWD”) and wireline logging. Downhole acoustic logging tools are often utilized to acquire various characteristics of earth formations traversed by the borehole. In such systems, acoustic waveforms are generated using a transmitter, and the acoustic responses are received using one or more receiver arrays. The acquired data is then utilized to determine the slownesses (velocities) of the formation and the borehole fluid, which could be used to calculate characteristics such as porosity, Poisson's ratio, Young's modulus and bulk modulus of the formation or the borehole fluid. Those characteristics may be of use in well planning and cement or formation evaluation; for example, to direct perforation guns or assess wellbore stability.
Borehole waves generated by an impulse source consist of multiple complicated guided waves travelling along the borehole surrounded by rock. To extract slowness measurements from those mixed wave motions, such as compressional slowness (“DTC”) and shear slowness (“DTS”), or shear slowness from low-frequency screw waves in LWD cases, a 2D coherence map is generally used for such purposes. However, the identification and correct picking of these target wave modes from the 2D map are challenging, as it is often necessary to deal with the a low signal-to-noise ratio (“SNR”), interferences of other wave modes, such as leaky-P wave, tool waves, Stoneley waves, road noises due to the tool movements, or aliases of these modes within the 2D coherence map. All of these reasons can contribute to a complicated borehole wave field, thus reducing the ability to make correct, simple and real-time automatic slowness picks.
Moreover, one of the primary challenges to acoustic data processing is the signal processor does not know whether the formation is hard or soft (i.e., formation type), as the borehole wave characteristics are quite different for these two types of formations. Generally, the system requires user input if shear waves exist. In real-time processing, the task becomes even more challenging as there is no human-computer interaction. Often, conventional processing of waveform data acquired with a single type of source (e.g., monopole source) is hard to distinguish if those waves after the refracted compressional waves are shear waves, mud waves, leaky-P waves, Stoneley waves or high-frequency pseudo-Rayleigh waves. Such multiple possibilities lead to the situation where the system cannot automatically pick and identify shear waves.